third_party/TopicFM/flop_counter.py

import torch
from fvcore.nn import FlopCountAnalysis
from einops.einops import rearrange

from src import get_model_cfg
from src.models.backbone import FPN as topicfm_featnet
from src.models.modules import TopicFormer
from src.utils.dataset import read_scannet_gray

from third_party.loftr.src.loftr.utils.cvpr_ds_config import default_cfg
from third_party.loftr.src.loftr.backbone import ResNetFPN_8_2 as loftr_featnet
from third_party.loftr.src.loftr.loftr_module import LocalFeatureTransformer


def feat_net_flops(feat_net, config, input):
    model = feat_net(config)
    model.eval()
    flops = FlopCountAnalysis(model, input)
    feat_c, _ = model(input)
    return feat_c, flops.total() / 1e9


def coarse_model_flops(coarse_model, config, inputs):
    model = coarse_model(config)
    model.eval()
    flops = FlopCountAnalysis(model, inputs)
    return flops.total() / 1e9


if __name__ == "__main__":
    path_img0 = "assets/scannet_sample_images/scene0711_00_frame-001680.jpg"
    path_img1 = "assets/scannet_sample_images/scene0711_00_frame-001995.jpg"
    img0, img1 = read_scannet_gray(path_img0), read_scannet_gray(path_img1)
    img0, img1 = img0.unsqueeze(0), img1.unsqueeze(0)

    # LoFTR
    loftr_conf = dict(default_cfg)
    feat_c0, loftr_featnet_flops0 = feat_net_flops(
        loftr_featnet, loftr_conf["resnetfpn"], img0
    )
    feat_c1, loftr_featnet_flops1 = feat_net_flops(
        loftr_featnet, loftr_conf["resnetfpn"], img1
    )
    print(
        "FLOPs of feature extraction in LoFTR: {} GFLOPs".format(
            (loftr_featnet_flops0 + loftr_featnet_flops1) / 2
        )
    )
    feat_c0 = rearrange(feat_c0, "n c h w -> n (h w) c")
    feat_c1 = rearrange(feat_c1, "n c h w -> n (h w) c")
    loftr_coarse_model_flops = coarse_model_flops(
        LocalFeatureTransformer, loftr_conf["coarse"], (feat_c0, feat_c1)
    )
    print(
        "FLOPs of coarse matching model in LoFTR: {} GFLOPs".format(
            loftr_coarse_model_flops
        )
    )

    # TopicFM
    topicfm_conf = get_model_cfg()
    feat_c0, topicfm_featnet_flops0 = feat_net_flops(
        topicfm_featnet, topicfm_conf["fpn"], img0
    )
    feat_c1, topicfm_featnet_flops1 = feat_net_flops(
        topicfm_featnet, topicfm_conf["fpn"], img1
    )
    print(
        "FLOPs of feature extraction in TopicFM: {} GFLOPs".format(
            (topicfm_featnet_flops0 + topicfm_featnet_flops1) / 2
        )
    )
    feat_c0 = rearrange(feat_c0, "n c h w -> n (h w) c")
    feat_c1 = rearrange(feat_c1, "n c h w -> n (h w) c")
    topicfm_coarse_model_flops = coarse_model_flops(
        TopicFormer, topicfm_conf["coarse"], (feat_c0, feat_c1)
    )
    print(
        "FLOPs of coarse matching model in TopicFM: {} GFLOPs".format(
            topicfm_coarse_model_flops
        )
    )